diff --git a/shellhist/core/patterns.py b/shellhist/core/patterns.py
index 7093ada..47983ac 100644
--- a/shellhist/core/patterns.py
+++ b/shellhist/core/patterns.py
@@ -1 +1,125 @@
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\ No newline at end of file
+"""Pattern detection algorithms for shell history."""
+
+from collections import defaultdict
+from dataclasses import dataclass, field
+from typing import Optional
+
+from shellhist.core import HistoryEntry, HistoryStore
+
+
+@dataclass
+class CommandPattern:
+    """Represents a detected command pattern."""
+    commands: list[str]
+    frequency: int
+    percentage: float = 0.0
+
+
+def detect_command_pairs(
+    store: HistoryStore,
+    min_frequency: int = 2,
+) -> list[CommandPattern]:
+    """Detect pairs of commands that frequently occur together."""
+    pairs: dict[tuple[str, str], int] = defaultdict(int)
+    entries = store.entries
+    
+    for i in range(len(entries) - 1):
+        if entries[i].shell_type == entries[i + 1].shell_type:
+            pair = (entries[i].command, entries[i + 1].command)
+            pairs[pair] += 1
+    
+    total_pairs = sum(pairs.values())
+    patterns = []
+    
+    for (cmd1, cmd2), count in pairs.items():
+        if count >= min_frequency:
+            percentage = (count / total_pairs * 100) if total_pairs > 0 else 0.0
+            patterns.append(CommandPattern(
+                commands=[cmd1, cmd2],
+                frequency=count,
+                percentage=percentage
+            ))
+    
+    patterns.sort(key=lambda x: x.frequency, reverse=True)
+    return patterns
+
+
+def detect_command_triplets(
+    store: HistoryStore,
+    min_frequency: int = 2,
+) -> list[CommandPattern]:
+    """Detect triplets of commands that frequently occur together."""
+    triplets: dict[tuple[str, str, str], int] = defaultdict(int)
+    entries = store.entries
+    
+    for i in range(len(entries) - 2):
+        if (entries[i].shell_type == entries[i + 1].shell_type == entries[i + 2].shell_type):
+            triplet = (entries[i].command, entries[i + 1].command, entries[i + 2].command)
+            triplets[triplet] += 1
+    
+    total_triplets = sum(triplets.values())
+    patterns = []
+    
+    for (cmd1, cmd2, cmd3), count in triplets.items():
+        if count >= min_frequency:
+            percentage = (count / total_triplets * 100) if total_triplets > 0 else 0.0
+            patterns.append(CommandPattern(
+                commands=[cmd1, cmd2, cmd3],
+                frequency=count,
+                percentage=percentage
+            ))
+    
+    patterns.sort(key=lambda x: x.frequency, reverse=True)
+    return patterns
+
+
+def detect_repetitive_commands(
+    store: HistoryStore,
+    min_frequency: int = 2,
+) -> list[CommandPattern]:
+    """Detect commands that are run repeatedly."""
+    patterns = []
+    
+    for command, freq in store.command_frequency.items():
+        if freq >= min_frequency:
+            total = len(store.entries)
+            percentage = (freq / total * 100) if total > 0 else 0.0
+            patterns.append(CommandPattern(
+                commands=[command],
+                frequency=freq,
+                percentage=percentage
+            ))
+    
+    patterns.sort(key=lambda x: x.frequency, reverse=True)
+    return patterns
+
+
+def ngram_analysis(
+    store: HistoryStore,
+    n: int = 2,
+    min_frequency: int = 2,
+) -> list[CommandPattern]:
+    """Analyze n-grams (sequences of n commands) in history."""
+    ngrams: dict[tuple[str, ...], int] = defaultdict(int)
+    entries = store.entries
+    
+    for i in range(len(entries) - n + 1):
+        shell_types = [entries[j].shell_type for j in range(i, i + n)]
+        if len(set(shell_types)) == 1:
+            ngram = tuple(entries[j].command for j in range(i, i + n))
+            ngrams[ngram] += 1
+    
+    total_ngrams = sum(ngrams.values())
+    patterns = []
+    
+    for ngram, count in ngrams.items():
+        if count >= min_frequency:
+            percentage = (count / total_ngrams * 100) if total_ngrams > 0 else 0.0
+            patterns.append(CommandPattern(
+                commands=list(ngram),
+                frequency=count,
+                percentage=percentage
+            ))
+    
+    patterns.sort(key=lambda x: x.frequency, reverse=True)
+    return patterns